Integrated knowledge-based hierarchical modelling of manufacturing organizations

The objective of this thesis is to research into an integrated knowledge-based simulation method, which combines the capability of knowledge based simulation and a structured analysis method, for the design and analysis of complex and hierarchical manufacturing organizations. This means manufacturing organizations analysed according to this methodology can manage the tactical and operational planning as well as the direct operation of shop floor. [Continues.

Position of discovered NTRs in the zebrafish genome.

<p>The USCS Zebrafish Genome Graphs tool was used to generate the figure. The 152 NTRs with approximate genomic positions (vertical lines in black) were detected on each chromosome (represented by grey bars) of the zebrafish genome. The NTRs marked with crosses were validated using RT-PCR, and those marked with asterisks were evaluated using qRT-PCR.</p

Systematic workflow for the identification of NTRs using RNA-Seq datasets.

<p>A. RNA-Seq reads form fragments (shown in green, blue, red and purple), which further generate clusters for NTR identification. D1: the distance between a putative NTR and any annotated transcribed regions; D2: the distance between two putative NTRs; B. Systematic workflow of NTR identification. The numbers for fragments are circled in blue, while numbers for clusters are in red circles. Singleton fragments are one-fragment clusters with length over 50 bp.</p

Prediction and validation of 4 randomly selected NTRs.

<p>The RNA samples used for validation were extracted from 50% epiboly. RNA-seq: RNA-Seq tracks (red boxes) based on the pooled RNA-Seq data; NTRs: Predicted structures of putative NTRs by our pipeline. Blue boxes represent fragments; Amplicons: Validations of the predicted NTR structures by RT-PCR. “Chr” indicates chromosome. A. NTR50; B. NTR88; C. NTR103; and D. NTR145.</p

Online_Appendix – Supplemental material for Bayesian DINA Modeling Incorporating Within-Item Characteristic Dependency

<p>Supplemental material, Online_Appendix for Bayesian DINA Modeling Incorporating Within-Item Characteristic Dependency by Peida Zhan, Hong Jiao, Manqian Liao and Yufang Bian in Applied Psychological Measurement</p

Putative NTRs without annotation in NCBI Zebrafish Annotation.

<p>Putative NTRs without annotation in NCBI Zebrafish Annotation.</p

Presentation_1_Probabilistic-Input, Noisy Conjunctive Models for Cognitive Diagnosis.pdf

<p>Existing cognitive diagnosis models conceptualize attribute mastery status discretely as either mastery or non-mastery. This study proposes a different conceptualization of attribute mastery as a probabilistic concept, i.e., the probability of mastering a specific attribute for a person, and developing a probabilistic-input, noisy conjunctive (PINC) model, in which the probability of mastering an attribute for a person is a parameter to be estimated from data. And a higher-order version of the PINC model is used to consider the associations among attributes. The results of simulation studies revealed a good parameter recovery for the new models using the Bayesian method. The Examination for the Certificate of Proficiency in English (ECPE) data set was analyzed to illustrate the implications and applications of the proposed models. The results indicated that PINC models had better model-data fit, smaller item parameter estimates, and more refined estimates of attribute mastery.</p

Taxation of corporates in the Czech Republic and Austria

This bachelor thesis aims to determine and compare the tax burden of corporates in terms of tax on corporate profits in the Czech Republic and Austria. It consists of two parts. The first one is focused on introduction of the corporate tax structure in both countries. The second part compares the tax burden using real and fictitious indicators. The analysis of all obtained values shows that Austria is subject of higher corporate tax burden. Despite that, for some companies, other aspects, such as the possibility of group taxation in Austria, may be relevant

Additional file 3: Table S3. of Genomic sequencing of a dyslexia susceptibility haplotype encompassing ROBO1

Rare coding heterozygous SNVs detected by both platforms in the linkage region. (DOC 28 kb

Image_2_Rare Copy Number Variants in Array-Based Comparative Genomic Hybridization in Early-Onset Skeletal Fragility.TIF

<p>Early-onset osteoporosis is characterized by low bone mineral density (BMD) and fractures since childhood or young adulthood. Several monogenic forms have been identified but the contributing genes remain inadequately characterized. In search for novel variants and novel candidate loci, we screened a cohort of 70 young subjects with mild to severe skeletal fragility for rare copy-number variants (CNVs). Our study cohort included 15 subjects with primary osteoporosis before age 30 years and 55 subjects with a pathological fracture history and low or normal BMD before age 16 years. A custom-made high-resolution comparative genomic hybridization array with enriched probe density in >1,150 genes important for bone metabolism and ciliary function was used to search for CNVs. We identified altogether 14 rare CNVs. Seven intronic aberrations were classified as likely benign. Five CNVs of unknown clinical significance affected coding regions of genes not previously associated with skeletal fragility (ETV1-DGKB, AGBL2, ATM, RPS6KL1-PGF, and SCN4A). Finally, two CNVs were pathogenic and likely pathogenic, respectively: a 4 kb deletion involving exons 1–4 of COL1A2 (NM_000089.3) and a 12.5 kb duplication of exon 3 in PLS3 (NM_005032.6). Although both genes have been linked to monogenic forms of osteoporosis, COL1A2 deletions are rare and PLS3 duplications have not been described previously. Both CNVs were identified in subjects with significant osteoporosis and segregated with osteoporosis within the families. Our study expands the number of pathogenic CNVs in monogenic skeletal fragility and shows the validity of targeted CNV screening to potentially pinpoint novel candidate loci in early-onset osteoporosis.</p